Statistics Training in Tashkent

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level engineers and designers who wish to use DFSS to ensure that quality is built into the product or process from the start of the design phase.

By the end of this training, participants will be able to:

• Understand the principles and concepts of Design for Six Sigma (DFSS).
• Learn the DMAIC (Define, Measure, Analyze, Improve, Control) and DMADV (Define, Measure, Analyze, Design, Verify) methodologies.
• Apply DFSS tools and techniques to design and optimize processes.
• Develop skills to manage and lead DFSS projects effectively.

This instructor-led, live training in Tashkent (online or onsite) targets advanced-level professionals seeking an in-depth understanding of the Six Sigma DMAIC methodology at the Black Belt level.

Upon completion of this training, participants will be able to:

• Develop a thorough understanding of the Define, Measure, Analyze, Improve, and Control phases, specifically tailored to complex projects.
• Acquire expertise in advanced statistical analysis techniques to support data-driven decision-making.
• Lead large-scale Six Sigma projects and manage project teams effectively.
• Implement significant process improvements that align with organizational goals and strategies.

R Markdown serves as an authoring framework that seamlessly integrates executable R code with narrative text to produce dynamic and reproducible documents.

This instructor-led live training, available both online and onsite, is designed for beginner to intermediate-level R users who aim to generate automated, dynamic reports using R Markdown for internal documentation, research publications, or web publishing.

Upon completion of this training, participants will be able to:

• Create R Markdown documents with embedded R code chunks and formatted text.
• Generate outputs in multiple formats including HTML, PDF, and Word.
• Use parameterized reports and conditional logic to generate dynamic content.
• Customize document appearance with themes, templates, and LaTeX for professional presentation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led live training in Tashkent (online or onsite) is aimed at intermediate-level professionals who wish to have a comprehensive understanding of the DMAIC methodology and prepare for the role of an effective Six Sigma Green Belt.

By the end of this training, participants will be able to:

• Gain a thorough knowledge of the Define, Measure, Analyze, Improve, and Control phases of Six Sigma.
• Apply a wide range of Six Sigma tools and techniques effectively within each phase of DMAIC.
• Perform and interpret statistical analyses for process improvement.
• Plan, execute, and manage a Six Sigma project.

This instructor-led, live training in Tashkent (online or onsite) is designed for beginner-level professionals seeking a fundamental understanding of Six Sigma and Lean principles to integrate them and enhance business processes.

By the end of this training, participants will be able to:

• Gain a solid understanding of Lean Six Sigma principles.
• Utilize Six Sigma methodologies to improve process quality by eliminating the causes of defects and minimizing variability.
• Combine Lean and Six Sigma methodologies to achieve more efficient and effective process improvements.
• Apply basic Lean Six Sigma tools and techniques that Yellow Belts can use in process improvement projects, such as 5S, Kaizen, and process mapping.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level professionals who wish to master both Six Sigma and Lean principles to lead significant process improvement initiatives within their organisations.

By the end of this training, participants will be able to:

• Develop a comprehensive understanding of how Lean and Six Sigma methodologies can be integrated for process improvement.
• Gain in-depth knowledge and practical application skills across the Define, Measure, Analyze, Improve, and Control phases.
• Apply advanced statistical tools for data-driven decision-making and process analysis.
• Effectively lead and manage Lean Six Sigma projects.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level professionals who wish to clean and analyze data, make statistical projections, and create insightful visualizations using these tools.

By the end of this training, participants will be able to:

• Understand the basics of Python, R, Power Query, and Power BI for data analysis.
• Clean and organize datasets using Python and Power Query.
• Perform statistical analysis and projections with R.
• Create professional dashboards and reports with Power BI.
• Integrate and analyze data from multiple sources effectively.

This instructor-led, live training in Tashkent (online or onsite) targets advanced professionals who wish to acquire the skills to manage complex Six Sigma projects and take on leadership roles in Six Sigma and Lean implementations.

By the end of this training, participants will be able to:

• Develop an in-depth understanding of advanced Six Sigma and Lean methodologies and tools.
• Lead complex process improvement projects that align with organizational strategies.
• Perform complex statistical analyses and make data-driven decisions.
• Effectively lead change initiatives and build a culture of continuous improvement.

This instructor-led, live training in Tashkent (online or onsite) is designed for beginner-level professionals who wish to gain a deeper understanding of the Six Sigma methodology and its principles, while mastering the DMAIC process.

By the end of this training, participants will be able to:

• Gain knowledge that extends beyond the basic principles covered in the White Belt course.
• Master the DMAIC process and learn advanced Six Sigma tools.
• Learn how to effectively contribute to a Six Sigma project team and collaborate with Green and Black Belts.
• Apply Six Sigma methodologies to real-world business processes for improvement.

Shiny is an R package that enables users to build interactive and dynamic web applications directly from R, without requiring knowledge of JavaScript or complex web frameworks.

This instructor-led, live training (online or onsite) is aimed at intermediate-level R users who wish to build and deploy custom web applications using Shiny for data visualization, user interaction, and reporting.

By the end of this training, participants will be able to:

• Create interactive web applications using Shiny and R.
• Design user interfaces with reactive elements and dynamic inputs.
• Incorporate data visualizations and reporting features into Shiny apps.
• Deploy and share Shiny apps locally or online via Shiny Server or Posit Cloud.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Real-time implementation in a live-lab environment.

Course Customization Options

• To request a customized version of this training, please contact us to arrange.

This guided, live training session in Tashkent (online or on-site) is tailored for novice professionals seeking a foundational grasp of the Six Sigma methodology and its core principles.

Upon completion of this training, participants will be equipped to:

• Grasp the fundamentals of Six Sigma and familiarize themselves with the DMAIC framework.
• Comprehend the distinct roles and duties associated with a White Belt in Six Sigma initiatives.
• Discover how to utilize Six Sigma principles to spot improvement prospects and bolster Six Sigma projects within their organization.

This instructor-led, live training in Tashkent (online or onsite) is aimed at scientists who wish to learn and use the Design of Experiments (DoE) to understand the cause-and-effect relationship between multiple factors.

By the end of this training, participants will be able to:

• Understand the advantages of designed experiments over other approaches.
• Understand the cause-and-effect relationships and interactions between factors.
• Learn the best practices and guidelines for conducting successful experimentation.

This instructor-led, live training in Tashkent (online or onsite) is designed for individuals seeking to learn and master the fundamentals of econometric analysis and modeling.

Upon completing this training, participants will be able to:

• Acquire and comprehend the core principles of econometrics.
• Effectively utilize Eviews and risk simulators.

R is a widely adopted, open-source environment for statistical computing, data analytics, and graphics. This course introduces students to the R programming language, covering its fundamentals, essential libraries, and advanced concepts. Participants will explore advanced data analytics and visualization techniques using real-world datasets.

Target Audience

Developers and data analysts

Duration

3 days

Format

Lectures combined with hands-on practical sessions

Target Audience

Analysts, researchers, scientists, graduates, students, and anyone interested in learning how to streamline statistical analysis using Microsoft Excel.

Course Objectives

This course will enhance your proficiency with Excel and statistics, thereby boosting the effectiveness and efficiency of your work or research.

The course explains how to utilise the Analysis ToolPak in Microsoft Excel, apply statistical functions, and perform fundamental statistical procedures. It also outlines Excel's limitations and provides practical strategies to overcome them.

In today's business landscape, data analytics is an indispensable tool. This course prioritizes the development of practical, hands-on skills for effective data analysis. Its primary goal is to empower participants to provide evidence-based answers to critical questions:

What has happened?

• processing and analyzing data
• producing informative data visualizations

What will happen?

• forecasting future performance
• evaluating forecasts

What should happen?

• turning data into evidence-based business decisions
• optimizing processes

It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Audience

Financial or market analysts, managers, accountants

Course Objectives

Facilitate and automate all kinds of financial analysis with Microsoft Excel

This instructor-led, live training in Tashkent (online or onsite) is aimed at data analysts who wish to program in R for Excel.

By the end of this training, participants will be able to:

• Toggle and move data between Excel and R.
• Use R Tidyverse and R features for data analytic solutions in Excel.
• Extend their data analytical skills by learning R.

R is a widely adopted programming language within the financial sector. It powers a broad spectrum of financial applications, from core trading systems to risk management platforms.

Through this instructor-led live training, participants will learn how to leverage R to create practical applications that address specific financial challenges.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts of the R programming language
• Select and employ R packages and techniques to organize, visualize, and analyze financial data from diverse sources (such as CSV, Excel, databases, and web APIs)
• Develop applications that solve problems related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize R applications

Audience

• Developers
• Analysts
• Quantitative Analysts (Quants)

Course Format

• A combination of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training is designed to provide solutions for some of the key issues faced by finance professionals. However, if there is a specific topic, tool, or technique you wish to add or expand upon, please contact us to make arrangements.

This instructor-led, live training (online or onsite) is aimed at HR professionals who wish to use analytical methods to improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level advanced R users who wish to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

By the end of this training, participants will be able to: create reusable functions, improve data workflows, debug and optimize code, and produce reproducible reports.

Objective:

Gain proficiency in independently utilizing SPSS for advanced applications, including managing dialog boxes and employing command language syntax for selected analytical techniques.

Target Audience:

This course is designed for analysts, researchers, scientists, students, and anyone seeking to enhance their SPSS capabilities to an advanced level and master specific statistical models. The training addresses universal analytical challenges while being tailored to the needs of specific industries.

This instructor-led, live training in Tashkent (online or onsite) is aimed at business analysts who wish to automate trading using algorithmic methods, Python, and R.

By the end of this training, participants will be able to:

• Use algorithms to buy and sell securities rapidly at specialized increments.
• Reduce trading-related costs through the use of algorithmic trading.
• Automatically monitor stock prices and execute trades.

Audience:

This course is designed for IT specialists seeking solutions to store and process large datasets within a distributed system environment.

Goal:

To develop in-depth expertise in Hadoop cluster administration.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level data scientists and statisticians who wish to effectively prepare data, build models, and apply machine learning techniques within their professional domains.

By the end of this training, participants will be able to:

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and effectively visualize results.
• Apply machine learning techniques to real-world, sector-specific scenarios.

The term 'Big Data' refers to solutions designed for the storage and processing of extensive data sets. Initially developed by Google, these Big Data solutions have evolved and inspired numerous similar initiatives, many of which are available as open-source projects. R is a widely adopted programming language within the financial industry.

This instructor-led live training in Tashkent (online or onsite) is aimed at beginner-level data analysts who wish to use R programming to manipulate data, perform basic data analysis, and create compelling visualizations for insights.

By the end of this training, participants will be able to:

• Understand the basics of R Programming.
• Apply fundamental data science processes.
• Create visual representations of data.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphical visualization. An increasing number of managers and data analysts across corporations and academic institutions rely on R. It has also gained popularity among statisticians, engineers, and scientists who lack formal programming skills but find the language intuitive and easy to use. R's growing adoption is driven by the rising demand for data mining to achieve diverse objectives, such as optimizing advertising prices, accelerating drug discovery, and refining financial models. The language offers a vast ecosystem of packages tailored for data mining tasks.

This instructor-led, live training in Tashkent (online or onsite) is designed for data analysts who wish to program using R within SAS to perform cluster analysis.

By the end of this training, participants will be able to:

• Apply cluster analysis for data mining purposes.
• Master R syntax for developing clustering solutions.
• Implement both hierarchical and non-hierarchical clustering techniques.
• Make data-driven decisions to enhance business operations.

This instructor-led, live training in Tashkent (online or on-site) is designed for beginner-level professionals who wish to master the fundamentals of R and learn how to effectively work with data.

By the end of this training, participants will be able to:

• Understand the R programming environment and the RStudio interface.
• Import, manipulate, and explore datasets using R commands and packages.
• Perform basic statistical analysis and data summarization.
• Create visualizations using both base R and the ggplot2 package.
• Efficiently manage workspaces, scripts, and packages.

This course is intended for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.

This instructor-led, live training in Tashkent (online or onsite) is tailored for data scientists who wish to program in Python and R for KNIME.

By the end of this training, participants will be able to:

• Plan, build, and deploy machine learning models in KNIME.
• Make data driven decisions for operations.
• Implement end to end data science projects.

This course offers a thorough introduction to Minitab, a robust statistical software tool widely used for data analysis and quality enhancement. Participants will gain skills in importing, managing, and analyzing data, along with creating basic graphical representations. By the end of the course, attendees will be equipped to conduct fundamental statistical analyses, including hypothesis testing and confidence intervals, using Minitab.

Target Audience

This course is designed for analysts and forecasters looking to introduce or enhance their forecasting capabilities in areas such as sales forecasting, economic forecasting, technology forecasting, supply chain management, and demand or supply forecasting.

Course Description

This program guides participants through various methodologies, frameworks, and algorithms that are valuable for predicting future trends based on historical data.

It utilizes standard tools such as Microsoft Excel or open-source software (particularly the R programming language).

The principles taught in this course can be applied using any compatible software (e.g., SAS, SPSS, Statistica, MINITAB, etc.).

This course is designed for anyone with an interest in statistical analysis. It introduces users to Minitab, enhancing the effectiveness and efficiency of your data analysis while deepening your understanding of statistical principles.

In this instructor-led, live training, participants will master advanced techniques for Machine Learning with R as they build a real-world application step by step.

By the end of this training, participants will be able to:

• Understand and implement unsupervised learning techniques
• Apply clustering and classification methods to generate predictions based on real-world data.
• Visualize data to quickly gain insights, support decision-making, and further refine analysis.
• Enhance the performance of a machine learning model through hyper-parameter tuning.
• Deploy a model into production for integration within a larger application.
• Apply advanced machine learning techniques to address challenges involving social network data, big data, and more.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. An increasing number of managers and data analysts in corporations and academic institutions rely on R. The language has also gained popularity among statisticians, engineers, and scientists who lack formal computer programming skills but find R intuitive and easy to use. Its widespread adoption stems from the growing use of data mining for diverse objectives, such as setting optimal ad prices, accelerating drug discovery, or refining financial models. R offers a comprehensive range of packages specifically tailored for data mining tasks.

R is a free, open-source programming language designed for statistical computing, data analysis, and graphics. A growing number of managers and data analysts in both corporate and academic settings are adopting R. It has also gained popularity among statisticians, engineers, and scientists without formal programming backgrounds due to its ease of use. R's rising prominence stems from the increasing reliance on data mining to achieve diverse objectives, such as setting optimal advertising prices, accelerating drug discovery, and refining financial models. The language offers a broad range of packages tailored for data mining tasks.

This course is designed for data scientists and statisticians who already possess basic R & C++ coding skills and are looking to enhance their expertise in advanced R programming.

The goal is to provide participants with practical, advanced R programming techniques that can be directly applied in their professional work.

The training incorporates sector-specific examples to ensure relevance and practical application for the audience.

This instructor-led, live training in Tashkent (online or onsite) is aimed at process department personnel and operations team members who wish to master practical process improvement techniques using Six Sigma principles and BPMN 2.0 modeling.

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to perform statistical analysis using SPSS to interpret data accurately, run complex statistical tests, and generate meaningful insights.

By the end of this training, participants will be able to:

• Navigate the SPSS interface and manage datasets efficiently.
• Perform descriptive and inferential statistical analyses.
• Conduct t-tests, ANOVA, MANOVA, regression, and correlation analyses.
• Apply non-parametric tests, principal component analysis, and factor analysis for advanced data interpretation.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level analysts, researchers, and professionals seeking to leverage SPSS for data preparation, statistical analysis, and predictive modeling.

This course is designed for decision-makers whose primary objective is not to perform calculations or analyses themselves, but to understand these processes and identify which statistical methods are most relevant for an organization's strategic planning.

For instance, a prospective participant may need to decide how many samples to collect before determining whether to launch a product.

If you require a more comprehensive course that covers the fundamentals of statistical thinking, please consider our 5-day 'Statistics for Managers' training.

This course is designed for individuals seeking to develop general statistical skills. It can be customized to align with specific professional fields such as market research, biology, manufacturing, public sector research, and others.

This course aims to provide researchers with a solid understanding of the principles of statistical design and analysis, as well as their relevance to research across a wide range of scientific disciplines.

The training covers key probability and statistical methods, primarily illustrated through practical examples. Approximately 30% of the course consists of lectures, while 70% is dedicated to guided quizzes and hands-on lab sessions.

For closed courses, examples and materials can be customised to fit a specific field, such as psychology, public sector research, biology, genetics, and more.

In public courses, a mix of examples from various disciplines is used.

Although the course utilises a range of software tools—from Microsoft Excel to SPSS and Statgraphics—its primary focus remains on developing a clear understanding of the principles and processes that guide research, reasoning, and conclusion-drawing.

This course can also be delivered in a blended format, incorporating homework and assignments.

This instructor-led, live training in Tashkent (online or on-site) is designed for advanced-level data professionals who wish to use Tableau to build complex visualizations, optimize dashboards, and perform advanced data analysis.

By the end of this training, participants will be able to install and configure Tableau for advanced use, create complex calculations, design interactive dashboards, and optimize performance.

This instructor-led live training in Tashkent (online or onsite) targets intermediate-level data professionals aiming to utilize Tableau for creating advanced visualizations and dashboards to drive data-informed decisions.

By the end of this training, participants will be able to: build advanced charts, utilize calculated fields and parameters, design interactive dashboards, and effectively share insights.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.

By the end of this training, participants will be able to:

• Understand the fundamentals of forecasting techniques in R.
• Apply exponential smoothing and ARIMA models for time series analysis.
• Utilize the ‘forecast’ package to generate accurate forecasting models.
• Automate forecasting workflows for business and research applications.

Target Audience

• managers
• developers
• scientists
• students

Course Format

Available as online instruction and discussion or face-to-face workshops

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. It is increasingly adopted by managers and data analysts within both corporate environments and academia. R has also gained popularity among statisticians, engineers, and scientists who may lack formal computer programming skills, as it is user-friendly. Its widespread use stems from the growing importance of data mining for various objectives, such as optimizing pricing strategies, accelerating drug discovery, and refining financial models. R offers a extensive array of packages tailored for data mining tasks.

This course focuses on manipulating objects in R, including data ingestion, accessing R packages, writing custom R functions, and creating informative visualizations. It also covers data analysis using standard statistical models. Participants will learn to utilize R software (https://www.r-project.org) via both the command line and a graphical user interface (GUI).